AI Enabled Intrusion Detection Systems for IoT Healthcare: Current Trends, Challenges, and Future Opportunities

One of the popular applications of the Internet of Things (IoT) is the IoT healthcare system. IoT healthcare system improves healthcare services by providing real-time patient monitoring and communication between patients and healthcare providers. IoT healthcare system uses different medical devices and sensors that send sensitive patient data to a centralized server or platform that uses different analytical approaches to assess patients’ health conditions but the communication of IoT devices suffers from privacy and security issues. As IoT applications are taking considerable growth, hackers are using different tactics to attack the systems. It can cause data breaches, operational damage, and reputational damage. A data breach leads to the exposure of sensitive health information and operational damage can interrupt the functionality of medical devices. IoT infrastructures are frequently targeted by threats such as Distributed Denial of Service (DDoS), Denial of Service (DoS), ransomware, and botnet intrusions. IoT systems require proactive security measures to protect them from such threats. One of the security solutions of IoT healthcare systems is the Intrusion Detection System (IDS). IDS alerts users or takes preventive measures by detecting suspicious behaviors or patterns. IDS has many challenges like less number of datasets, misjudgment, and lack of real-time response, etc., so researchers are working on that. In recent times, there are lots of artificial intelligence-based IoT healthcare system Intrusion Detection System has been proposed. In this chapter, we present a case study on various artificial intelligence algorithms and datasets for IoT healthcare system IDS. This chapter will help researchers to see the current trends in algorithms and datasets and also help to find scopes to work in the future.